There is a new exciting feature coming to JavaScript in the not-so-far future. That feature is Optional Chaining. At this moment, Optional Chaining is in Stage 3 of the TC39 process, so it’s in late stages of the process and will be here soonish.

In general terms, Optional Chaining is an approach to simplify JavaScript expressions for accessing deeply nested values, array items, and methods when there is a possibility that a reference may be missing.

In this blog, we give an introduction to Optional Chaining in JavaScript. We discuss what problems Optional Chaining solves, the various ways you can use it, and relatable code examples.

Both React and Angular are very popular front end development frameworks. In this post, I will discuss the similarities and differences between the two, and consider when one should be used instead of the other.

React is an open-source JavaScript library introduced by Facebook to build dynamic user interfaces. It is based on JavaScript and JSX (a PHP extension) and is considered widely for developing reusable HTML elements for front-end development.

Angular is an open-source front-end development framework powered by Google. It is a part of the MEAN stack and is compatible with a large number of code editors and is considered for creating dynamic websites and web apps.

In this post, we will begin by going over the benefits of React and Angular, then break down the differences between the two frameworks using thirteen attributes. By comparing each framework side by side, it can help decide which is the best framework for your specific app project.

This blog is about my dalliance with Elm; a purely functional, statically typed language that has type inference. It compiles to JavaScript. Functional programming is compelling, but heretofore, I’d only woven cherry-picked techniques into large object-oriented projects. In FP parlance, I’m partially applied! The times, they are a-changin’.

In this article, I’ll:
– touch on the reasoning for giving a nod to functional languages and data immutability;
– move on to Elm; a blazing-fast, statically typed, purely functional browser-side language that compiles to JavaScript and follows the principles of functional reactive programming;
– survey background items and the Elm environment;
– show a simple type-and-click application, followed by a more realistic To-do application;
– end with my impressions from functional-programming semi-outsider point-of-view.

So you have mastered Spring Boot and started toying around with React. Now you want React to talk to your Boot app as your back-end API. That’s fabulous. You probably already know how to do this, but there is a kicker. You want to package them and start both of them as just one project.

Well, you’re in luck! This blog is going to take a couple of simple projects and combine them into one project. Lace up your boots and get ready to React!

Machine Learning enables a system to automatically learn and progress from experience without being explicitly programmed. It’s a subset of the artificial intelligence (AI) technology space being applied and used throughout your everyday life. Think Siri, Alexa, toll booth scanners, text transcription of voicemails – these types of tools are used by just about everyone.

Image recognition and computer vision are also widely being used in production; recently just heard that Los Angeles, CA has made it illegal for law enforcement to use face recognition technology in its numerous public video cameras. The current state of the art allows real-time identification.

Interestingly, the algorithms and know-how for Machine Learning have been around for a long time. Artificial Intelligence was coined and researched as far back as the late 1950s, the advent of the digital computer, and expert systems and neural networks, that theoretically mimics how our brain learns.

The increase in Machine Learning production-ready applications started around 2012, with increased processing, bandwidth, and internet throughput power. This is important as deep learning algorithms like Neural Networks require lots of data and FPUs/GPUs to train.

In this blog, we introduce a conceptual overview of Neural Networks with a simple Neural Net code example implementation using Go. We will interact with it by building a ReactJS interface and train the Neural Network to recognize hand-drawn images of the numbers 0-9. Let’s dive in….